Multi-Sorted Inverse Frequent Itemsets Mining: On-Going Research

Inverse frequent itemset mining (IFM) consists of generating artificial transactional databases reflecting patterns of real ones, in particular, satisfying given frequency constraints on the itemsets. An extension of IFM called many-sorted IFM, is introduced where the schemes for the datasets to be generated are those typical of Big Tables, as required in emerging big data applications, e.g., social network analytics

Inverse Tree-OLAP: Definition, Complexity and First Solution

Count constraint is a data dependency that requires the results of given count operations on a relation to be within a certain range. By means of count constraints a new decisional problem, called the Inverse OLAP, has been recently introduced: given a flat fact table, does there exist an instance satisfying a set of given count constraints? This paper focus on a special case of Inverse OLAP, called Inverse Tree-OLAP, for which the flat fact table key is modeled by a Dimensional Fact Model (DFM) with a tree structure

Machine Learning Methods for Generating High Dimensional Discrete Datasets

The development of platforms and techniques for emerging Big Data and Machine Learning applications requires the availability of real-life datasets. A possible solution is to synthesize datasets that reflect patterns of real ones using a two-step approach: first, a real dataset X is analyzed to derive relevant patterns Z and, then, to use such patterns for reconstructing a new dataset X\u27 that preserves the main characteristics of X. This survey explores two possible approaches: (1) Constraint-based generation and (2) probabilistic generative modeling. The former is devised using inverse mining (IFM) techniques, and consists of generating a dataset satisfying given support constraints on the itemsets of an input set, that are typically the frequent ones. By contrast, for the latter approach, recent developments in probabilistic generative modeling (PGM) are explored that model the generation as a sampling process from a parametric distribution, typically encoded as neural network. The two approaches are compared by providing an overview of their instantiations for the case of discrete data and discussing their pros and cons

Generating Synthetic Discrete Datasets with Machine Learning

The real data are not always available/accessible/sufficient or in many cases they are incomplete and lacking in semantic content necessary to the definition of optimization processes. In this paper we discuss about the synthetic data generation under two different perspectives. The core common idea is to analyze a limited set of real data to learn the main patterns that characterize them and exploit this knowledge to generate brand new data. The first perspective is constraint-based generation and consists in generating a synthetic dataset satisfying given support constraints on the real frequent patterns. The second one is based on probabilistic generative modeling and considers the synthetic generation as a sampling process from a parametric distribution learned on the real data, typically encoded as a neural network (e.g. Variational Autoencoders, Generative Adversarial Networks)

cuTS: Scaling Subgraph Isomorphism on Distributed Multi-GPU Systems Using Trie Based Data Structure

Subgraph isomorphism is a pattern-matching algorithm widely used in many domains such as chem-informatics, bioinformatics, databases, and social network analysis. It is computationally expensive and is a proven NP-hard problem. The massive parallelism in GPUs is well suited for solving subgraph isomorphism. However, current GPU implementations are far from the achievable performance. Moreover, the enormous memory requirement of current approaches limits the problem size that can be handled. This work analyzes the fundamental challenges associated with processing subgraph isomorphism on GPUs and develops an efficient GPU implementation. We also develop a GPU-friendly trie-based data structure to drastically reduce the intermediate storage space requirement, enabling large benchmarks to be processed. We also develop the first distributed sub-graph isomorphism algorithm for GPUs. Our experimental evaluation demonstrates the efficacy of our approach by comparing the execution time and number of cases that can be handled against the state-of-the-art GPU implementations

An Effective and Efficient Graph Representation Learning Approach for Big Graphs

In the Big Data era, large graph datasets are becoming increasingly popular due to their capability to integrate and interconnect large sources of data in many fields, e.g., social media, biology, communication networks, etc. Graph representation learning is a flexible tool that automatically extracts features from a graph node. These features can be directly used for machine learning tasks. Graph representation learning approaches producing features preserving the structural information of the graphs are still an open problem, especially in the context of large-scale graphs. In this paper, we propose a new fast and scalable structural representation learning approach called SparseStruct. Our approach uses a sparse internal representation for each node, and we formally proved its ability to preserve structural information. Thanks to a light-weight algorithm where each iteration costs only linear time in the number of the edges, SparseStruct is able to easily process large graphs. In addition, it provides improvements in comparison with state of the art in terms of prediction and classification accuracy by also providing strong robustness to noise data

Ruptured Left Subclavian Artery Aneurysm in a 41-Year-Old Woman with Neurofibromatosis Type 1

Abstract Introduction Intrinsic lesions of the arterial wall are important manifestations of Neurofibromatosis type 1. Report A 41-year-old woman with Neurofibromatosis type 1, suffering sudden onset of upper back as well as left shoulder and upper chest pain is addressed to our hospital. The contrast-enhanced thoracic computed tomogram demonstrated a huge hematoma due to ruptured left subclavian artery aneurysm treated with endovascular therapy. Discussion A ruptured left subclavian artery is an uncommon but life threatening manifestation in Neurofibromatosis type 1

BERT prescriptions to avoid unwanted headaches : a comparison of transformer architectures for adverse drug event detection

Pretrained transformer-based models, such as BERT and its variants, have become a common choice to obtain state-of-the-art performances in NLP tasks. In the identification of Adverse Drug Events (ADE) from social media texts, for example, BERT architectures rank first in the leaderboard. However, a systematic comparison between these models has not yet been done. In this paper, we aim at shedding light on the differences between their performance analyzing the results of 12 models, tested on two standard benchmarks. SpanBERT and PubMedBERT emerged as the best models in our evaluation: this result clearly shows that span-based pretraining gives a decisive advantage in the precise recognition of ADEs, and that in-domain language pretraining is particularly useful when the transformer model is trained just on biomedical text from scratch

Chemical exchange saturation transfer MRI shows low cerebral 2-deoxy-D-glucose uptake in a model of Alzheimer's Disease

Glucose is the central nervous system's only energy source. Imaging techniques capable to detect pathological alterations of the brain metabolism are useful in different diagnostic processes. Such techniques are also beneficial for assessing the evaluation efficacy of therapies in pre-clinical and clinical stages of diseases. Chemical exchange saturation transfer (CEST) magnetic resonance imaging (MRI) is a possible alternative to positron emission tomography (PET) imaging that has been widely explored in cancer research in humans and animal models. We propose that pathological alterations in brain 2-deoxy-D-glucose (2DG) uptake, typical of neurodegenerative diseases, can be detected with CEST MRI. Transgenic mice overexpressing a mutated form of amyloid precusrsor protein (APP23), a model of Alzheimer's disease, analyzed with CEST MRI showed a clear reduction of 2DG uptake in different brain regions. This was reminiscent of the cerebral condition observed in Alzheimer's patients. The results indicate the feasibility of CEST for analyzing the brain metabolic state, with better image resolution than PET in experimental models